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Record W6889189236 · doi:10.25384/sage.c.6782841

Enhancing involvement of people with multiple sclerosis in clinical trial design

2023· other· en· W6889189236 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSage Journals Data · 2023
Typeother
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsClinical trialClinical study designMultiple sclerosisAlternative medicineResearch designPublic healthClinical PracticeAdaptive design

Abstract

fetched live from OpenAlex

Background:Although often overlooked, patient and public involvement (PPI) is vital when considering the design and delivery of complex and adaptive clinical trial designs for chronic health conditions such as multiple sclerosis (MS).Methods:We conducted a rapid review to assess current status of PPI in the design and conduct of clinical trials in MS over the last 5 years. We provide a case study describing PPI in the development of a platform clinical trial in progressive MS.Results:We identified only eight unique clinical trials that described PPI as part of articles or protocols; nearly, all were linked with funders who encourage or mandate PPI in health research. The OCTOPUS trial was co-designed with people affected by MS. They were central to every aspect from forming part of a governance group shaping the direction and strategy, to the working groups for treatment selection, trial design and delivery. They led the PPI strategy which enabled a more accessible, acceptable and inclusive design.Conclusion:Active, meaningful PPI in clinical trial design increases the quality and relevance of studies and the likelihood of impact for the patient community. We offer recommendations for enhancing PPI in future MS clinical trials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.678
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.225
GPT teacher head0.352
Teacher spread0.127 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it